Author details

Dinh Hoa Nguyen<sup>1</sup> \*, Huynh Ngoc Tran<sup>2</sup> , Tatsuo Narikiyo<sup>2</sup> and Michihiro Kawanishi<sup>2</sup>

1 International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) and Institute of Mathematics for Industry (IMI), Kyushu University, Fukuoka, Japan

References

2018]

179-197

152-178

342-351

397-408

49

[1] U.S. Department of Energy. The Smart Grid: An Introduction [Internet]. Available from: https://www.energy. gov/oe/downloads/smart-grid-

DOI: http://dx.doi.org/10.5772/intechopen.84136

[8] Samadi P, Mohsenian-Rad AH, Schober R, Wong VWS, Jatskevich J. Optimal real-time pricing algorithm based on utility maximization for smart grid. In: Proceedings of the First IEEE International Conference on Smart Grid Communications; 4-6 October 2010;

New York, USA: IEEE; 2010.

[9] Samadi P, Mohsenian-Rad H, Schober R, Wong VWS. Advanced demand side management for the future smart grid using mechanism design. IEEE Transactions on Smart Grid. 2012;

[10] Zhang W, Xu Y, Liu W, Zang C, Yu H. Distributed online optimal energy management for smart grids. IEEE Transactions on Industrial Informatics.

[11] Rahbari-Asr N, Ojha U, Zhang Z, Chow MY. Incremental welfare consensus algorithm for cooperative distributed generation/demand response in smart grid. IEEE

Transactions on Smart Grid. 2014;5(6):

[13] Xu Y, Yang Z, Gu W, Li M, Deng Z. Robust real-time distributed optimal control based energy management in a smart grid. IEEE Transactions on Smart

[12] Li N, Chen L, Low SH. Optimal demand response based on utility maximization in power networks. In: Proceedings of IEEE Power Energy Society General Meeting (2011 IEEE PESGM); 24-29 July 2011; New York,

USA: IEEE; 2011. pp. 1-8

Grid. 2017;8(4):1568-1579

2049-2061

[14] Zhao C, He J, Cheng P, Chen J. Consensus-based energy management in smart grid with transmission losses and directed communication. IEEE Transactions on Smart Grid. 2017;8(5):

pp. 415-420

A Distributed Optimization Method for Optimal Energy Management in Smart Grid

3(3):1170-1180

2015;11(3):717-727

2836-2845

introduction-0 [Accessed: October 28,

[2] Erol-Kantarci M, Mouftah HT. Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues. IEEE Communication Surveys and Tutorials. 2015;17(1):

[3] Vardakas JS, Zorba N, Verikoukis CV. A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Communication Surveys and Tutorials. 2015;17(1):

[4] Palensky P, Dietrich D. Demand side

[5] Esther BP, Kumar KS. A survey on residential demand side management architecture, approaches, optimization models and methods. Renewable and Sustainable Energy Reviews. 2016;59:

[6] U.S. Department of Energy. Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them [Internet]. 2006. Available from: https://emp. lbl.gov/sites/all/files/report-lbnl-1252d.pdf [Accessed: October 28, 2018]

[7] Loia V, Vaccaro A. Decentralized economic dispatch in smart grids by self-organizing dynamic agents. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2014;44(4):

management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial

Informatics. 2011;7(3):381-388

2 Department of Advanced Science and Technology, Toyota Technological Institute, Nagoya, Japan

\*Address all correspondence to: hoa.nd@i2cner.kyushu-u.ac.jp

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Distributed Optimization Method for Optimal Energy Management in Smart Grid DOI: http://dx.doi.org/10.5772/intechopen.84136
